Trust Incident Account Model: Preliminary Indicators for Trust Rhetoric and Trust or Distrust in Blogs
Bibliographic record
Abstract
This paper defines a concept of trust incident accounts as verbal reports of empirical episodes in which a trustor has reached a state of positive or negative expectations of a trustee’s behavior under associated risks. Such expectations are equated to trust and distrust, correspondingly, and present a sharp contrast with hypocritical use of trust rhetoric with ulterior motives such as an attempt to manipulate readers or gain trustworthiness. Distinguishing the three: trust, distrust, and trust rhetoric, is formulated as a new challenge in sentiment analysis and opinion-mining. Based on a preliminary exploration of trust narratives in blogs, 14 categories of textual indicators were identified manually. The finer-grain analytical model of trust incident accounts is proposed to include 12 information extraction frame components: trustor, trustee, source, textual clue, trust valence, risks, reasons, actions, trustor-trustee relationship, narrow context, broad domain, and complements. The study draws a cross-disciplinary theoretical bridge from social science and information technology trust literature to opinion-mining, and emphasizes the value of understanding trust in longer-term social relations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".